E1 244: Detection and Estimation Theory (3:0)InstructorVaibhav Katewa Class TimingsTueThu, 2:003:30 PM. First class on January 04, 2022. VenueOnline on Microsoft Teams. The link to join the class lectures is here. The link to join the Teams group is here. Class LogisticsWe will have live lectures on Microsoft Teams. These lectures will be recorded and made available to the students after each class along with the handwritten notes. The registered students will be added to a Class group in Microsoft Teams. All the course correspondence will happen in this Teams group. Teaching Assistants
Course Overview and SyllabusThis is a graduate level course on statistical inference that deals with decision making based on observed data. The course is divided into two parts  Detection Theory and Estimation Theory. Detection theory provides a framework to make an intelligent guess regarding which hypothesis is true among a given set of n>2 hypotheses, while Estimation Theory provides a framework to intelligently guess the value of an unknown parameter that can be random or deterministic. The students will learn to mathematically formulate appropriate detection and estimation problems, solve these problems to get good/best detectors and estimators, and analyze their performance. This is a mathoriented course and will use concepts from probability, random processes and linear algebra. We will broadly cover the following topics:
Prerequisites
Grading
ReferencesThere is no required textbook for the course. Below is a list of useful reference books:
